



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Optional[int]								=     'Wav2Vec2FeatureExtractor'
				A_   :     Optional[Any]								=     'AutoTokenizer'


				def __init__(							self      :  int						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Union[str, Any]       )		->     Union[str, Any]:
											super().__init__(__UpperCamelCase						, __UpperCamelCase       )
											A      						=    self.feature_extractor
											A      						=    False


				@classmethod
				def __UpperCamelCase						(							cls      :  List[Any]						, __UpperCamelCase      :  List[str]						, **__UpperCamelCase      :  str       )		->     Union[str, Any]:
											try:
																		return super().from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
											except OSError:
																		warnings.warn(
																		    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
																		    ' include a `tokenizer_class` attribute is deprecated and will be '
																		    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
																		    ' attribute to either your `config.json` or `tokenizer_config.json` '
																		    'file to suppress this warning: '						, __UpperCamelCase						, )

																		A      						=    WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																		A      						=    WavaVecaCTCTokenizer.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )

																		return cls(feature_extractor=__UpperCamelCase						, tokenizer=__UpperCamelCase       )


				def __call__(							self      :  Optional[int]						, *__UpperCamelCase      :  List[str]						, **__UpperCamelCase      :  Dict       )		->     Optional[Any]:
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor(*__UpperCamelCase						, **__UpperCamelCase       )

											if "raw_speech" in kwargs:
																		warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
																		A      						=    kwargs.pop('raw_speech'       )
											else:
																		A      						=    kwargs.pop('audio'						, __UpperCamelCase       )
											A      						=    kwargs.pop('sampling_rate'						, __UpperCamelCase       )
											A      						=    kwargs.pop('text'						, __UpperCamelCase       )
											if len(__UpperCamelCase       ) > 0:
																		A      						=    args[0]
																		A      						=    args[1:]

											if audio is None and text is None:
																		raise ValueError('You need to specify either an `audio` or `text` input to process.'       )

											if audio is not None:
																		A      						=    self.feature_extractor(__UpperCamelCase						, *__UpperCamelCase						, sampling_rate=__UpperCamelCase						, **__UpperCamelCase       )
											if text is not None:
																		A      						=    self.tokenizer(__UpperCamelCase						, **__UpperCamelCase       )

											if text is None:
																		return inputs
											elif audio is None:
																		return encodings
											else:
																		A      						=    encodings['input_ids']
																		return inputs


				def __UpperCamelCase						(							self      :  List[str]						, *__UpperCamelCase      :  List[str]						, **__UpperCamelCase      :  Union[str, Any]       )		->     Union[str, Any]:
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor.pad(*__UpperCamelCase						, **__UpperCamelCase       )

											A      						=    kwargs.pop('input_features'						, __UpperCamelCase       )
											A      						=    kwargs.pop('labels'						, __UpperCamelCase       )
											if len(__UpperCamelCase       ) > 0:
																		A      						=    args[0]
																		A      						=    args[1:]

											if input_features is not None:
																		A      						=    self.feature_extractor.pad(__UpperCamelCase						, *__UpperCamelCase						, **__UpperCamelCase       )
											if labels is not None:
																		A      						=    self.tokenizer.pad(__UpperCamelCase						, **__UpperCamelCase       )

											if labels is None:
																		return input_features
											elif input_features is None:
																		return labels
											else:
																		A      						=    labels['input_ids']
																		return input_features


				def __UpperCamelCase						(							self      :  List[str]						, *__UpperCamelCase      :  Union[str, Any]						, **__UpperCamelCase      :  Union[str, Any]       )		->     Union[str, Any]:
											return self.tokenizer.batch_decode(*__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple						, *__UpperCamelCase      :  List[str]						, **__UpperCamelCase      :  str       )		->     Dict:
											return self.tokenizer.decode(*__UpperCamelCase						, **__UpperCamelCase       )


				@contextmanager
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[Any]:
											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your audio inputs, or in a separate call.'       )
											A      						=    True
											A      						=    self.tokenizer
											yield
											A      						=    self.feature_extractor
											A      						=    False





'''simple docstring'''


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase_					=   logging.getLogger()


def     SCREAMING_SNAKE_CASE							(   a_						:				Path					,       a_						:				list      ):
  __a								=       '\n'.join(a_      )
  Path(a_      ).open('w'      ).writelines(a_      )


UpperCAmelCase_					=   "patrickvonplaten/t5-tiny-random"
UpperCAmelCase_					=   "sshleifer/bart-tiny-random"
UpperCAmelCase_					=   "sshleifer/tiny-mbart"

UpperCAmelCase_					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __a								=       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       str(Path(self.get_auto_remove_tmp_dir()   ) / 'scores.json'   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       run_generate()
       assert Path(UpperCamelCase   ).exists()
       # os.remove(Path(output_file_name))







   def 		UpperCamelCase__      (						self   )					->     int:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([BART_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Union[str, Any]:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([T5_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __a								=       {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __a								=       Path(self.get_auto_remove_tmp_dir()   )
     __a								=       str(tmp_dir / 'scores.json'   )
     __a								=       str(tmp_dir / 'val.target'   )
     _dump_articles(UpperCamelCase   ,				text['en']   )
     _dump_articles(UpperCamelCase   ,				text['de']   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase   )}\n            {str(UpperCamelCase   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']   )

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       with CaptureStdout() as cs:
         run_search()
       __a								=       [' num_beams | length_penalty', model, 'Best score args']
       __a								=       ['Info']
       if "translation" in task:
         expected_strings.append('bleu'   )
       else:
         expected_strings.extend(UpperCamelCase   )
       for w in expected_strings:
         assert w in cs.out
       for w in un_expected_strings:
         assert w not in cs.out
       assert Path(UpperCamelCase   ).exists()
       os.remove(Path(UpperCamelCase   )   )



